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检索条件"任意字段=2022 International Conference on Algorithms, Data Mining, and Information Technology, ADMIT 2022"

共 4127 条记录，以下是41-50 订阅

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A novel InfluxDB-based inverted index method 24

A novel InfluxDB-based inverted index method

引用

3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Hou, Zhuohan Huang, Qizhi Song, Yuanfeng Fan, Shuhuan Nie, Xiaowen School of ISE University of Electronic Science and Technology of China China Information Center University of Electronic Science and Technology of China China School of CSE University of Electronic Science and Technology of China China

ISBN: (纸本)9798400718120

High performance time series database supporting petabytes of data per day;however, the database cannot search the log’s text content. Therefore, this paper proposes an inverted index method for the InfluxDB time series data to improve the database’s text data searching ability. The method comprises two index levels: the first level uses lock-free hopping tables to screen out potential time series data, and the second utilizes an inverted log content index to accelerate specific time points data within the series. Furthermore, the experiments demonstrate that the modified InfluxDB method possesses a text matching function that increases the log text’s search speed by 13∼15.6 times that of the InfluxDB while ensuring the ability to read and write time series data remains unchanged. © 2024 Copyright held by the owner/author(s).

关键词： InfluxDB

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Empirical analysis of enabling high-quality development of digital economy based on grey correlation degree and Moran’s I index 24

Empirical analysis of enabling high-quality development of d...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Cao, Qian Wang, Xiaocong Institute of Science and Technology Information Beijing Academy of Science and Technology China Institute of Technology and Economics of Grain Industry Academy of National Food and Strategic Reserves Administration China

ISBN: (纸本)9798400718120

The index system of digital economy and high-quality development is constructed from multiple dimensions, and the development index of digital economy and high-quality development index are measured by entropy weight method. The correlation between digital economy and high-quality development is analyzed by using grey relational degree, and the spatial correlation characteristics of the two are analyzed by Moran’s I index. The study found that the development level and high quality of our country’s digital economy show an increasing trend of fluctuation, and the regional difference is obvious. The digital economy is highly correlated with high-quality development, with significant regional heterogeneity and spatial correlation characteristics. We should vigorously develop the digital economy and promote high-quality development. © 2024 Copyright held by the owner/author(s).

关键词： data quality

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Handle-based power terminal access authentication 24

Handle-based power terminal access authentication

引用

3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Wang, Xu Zhang, Dahua Hu, Baiji Yao, Shuang Jiang, Lin Yu, Yiying Information and Communication Department China Electrical Power Research Institute Department Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400718120

This paper introduces a handle-based access authentication scheme for power terminals, leveraging the Chinese SM2 cryptographic algorithm. The scheme utilizes the robust security features of SM2 to enhance the authentication process for power terminal devices. In the registration phase, each terminal receives a unique handle identifier, which, combined with the SM2 algorithm, ensures secure and efficient key management and authentication. This approach not only provides strong protection against various cyber threats but also simplifies the authentication process, making it ideal for large-scale smart grid deployments. The proposed scheme exhibits high security, efficiency, and scalability, meeting the critical need for reliable access control in power terminal systems. © 2024 Copyright held by the owner/author(s).

关键词： Identification authentication

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Detecting Unseen Anomalies in Dynamic Graph 24

Detecting Unseen Anomalies in Dynamic Graph

引用

3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Wang, Wenkai Zuo, Zeyu Wang, Meihong Wang, Pengfei School of Informatics Xiamen University Fujian Xiamen China Aerospace Science and Industry Network Information Development Co. Ltd. Fujian Xiamen China

ISBN: (纸本)9798400718120

In the current era of rapid advancements in information technology, social media serves as a crucial medium for information dissemination and communication. However, detecting and uncovering social risks within this expansive and dynamic online landscape poses significant challenges. Traditional anomaly detection methods, which rely on predefined patterns, face difficulties in identifying unknown or previously unseen types of anomalies, particularly amid evolving threats. To address these challenges, we propose an anomaly detection framework DGSVAD, which integrates the strengths of Temporal Graph Attention (TGAT) and Deep One-Class Support Vector data Description (Deep One-Class SVDD). TGAT is proficient at capturing dynamic features by adaptively updating graph structures and node features over time. Deep One-Class SVDD, in contrast, requires solely normal data for training, constructing a boundary around the normal data distribution to detect potential anomalies without relying on predefined categories. We evaluate the DGSVAD’s effectiveness through experiments and analysis on three real-world dataset. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Research on Network Quality Evaluation System Based on the Entropy Weight Method 24

Research on Network Quality Evaluation System Based on the E...

引用

3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Ming, Tong Man, Zhu A. Network Information Center Wuhan University of Technology China

ISBN: (纸本)9798400718120

Higher education campus networks, as a crucial infrastructure for educational informatization, are experiencing accelerated construction and development. Network quality has become a key indicator for measuring the level of network services. This study aims to establish a scientific and rational network quality evaluation system to objectively reflect the true performance of network services. In this research, we first clarified the research approach and technical route to ensure the scientific and systematic nature of the network quality evaluation system. Through extensive literature review and technical investigation, as well as statistical analysis of survey questionnaires using the Likert scale method and calculation of Kendall’s coefficient of concordance, we selected a series of key network performance indicators that cover multiple dimensions of the network. Based on this, we employed the entropy weight method to construct the network quality evaluation system. This method involves standardizing the collected data and calculating the entropy value of each indicator to quantify their weight in the evaluation system, thereby ensuring that the evaluation results can objectively reflect the true performance of the network. Furthermore, the effectiveness and practicality of the evaluation system were verified through case empirical comparison. © 2024 Copyright held by the owner/author(s).

关键词： Computer network performance evaluation

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2nd international conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2022

2nd International Conference on Ubiquitous Computing and Int...

引用

2nd international conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2022

ISBN: (纸本)9789811925405

The proceedings contain 57 papers. The special focus in this conference is on Ubiquitous Computing and Intelligent information Systems. The topics include: Survey on Handwritten Characters Recognition in Deep Learning;a Survey on Wild Creatures Alert System to Protect Agriculture Lands Domestic Creatures and People;a Study on Surveillance System Using Deep Learning Methods;IRHA: An Intelligent RSSI Based Home Automation System;a Review Paper on Machine Learning Techniques and Its Applications in Health Care Sector;Enhanced Mask-RCNN for Ship Detection and Segmentation;data Scientist Job Change Prediction Using Machine Learning Classification Techniques;Error Correction Scheme with Decimal Matrix Code for SRAM Emulation TCAMs;knowledge Discovery in Web Usage Patterns Using Pageviews and data mining Association Rule;a Six-Point Based Approach for Enhanced Broadcasting Using Selective Forwarding Mechanism in Mobile Ad Hoc Networks;video Anomaly Detection Using Optimization Based Deep Learning;a Fusional Cubic-Sine Map Model for Secure Medical Image Transmission;Innovative Technologies Developed for Autonomous Marine Vehicles by ENDURUNS Project;machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective;A Comparative Review on Image Analysis with Machine Learning for Extended Reality (XR) Applications;SWOT Analysis of Behavioural Recognition Through Variable Modalities;e-Mixup and Siamese Networks for Musical Key Estimation;microarray data Classification Using Feature Selection and Regularized Methods with Sampling Methods;visual Place Recognition Using Region of Interest Extraction with Deep Learning Based Approach;electronic Mobility Aid for Detection of Roadside Tree Trunks and Street-Light Poles;crop Price Prediction Using Machine Learning Naive Bayes algorithms;enneaontology: A Proposed Enneagram Ontology.

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Linking High-Risk Companies to Special Treatment (ST) Status: A Large Language Model Approach for Corporate Risk Evaluation 24

Linking High-Risk Companies to Special Treatment (ST) Status...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Wang, Hua Kang, Zhaoyu International Business School Zhejiang University Haining China Zhejiang University-University of Illinois Urbana-Champaign Zhejiang University Haining China

ISBN: (纸本)9798400718120

Traditional corporate risk analysis has primarily focused on quantitative financial data, often overlooking qualitative factors such as sustainability and corporate governance. This study leverages large language models (LLMs) to assess risk in three non-quantitative sections of A-share companies’ annual reports: Management Discussion and Analysis, Corporate Governance, and Environmental and Social Responsibility. We analyzed annual reports from 200 companies using the ChatGLM-4 model, which was pre-trained on a multilingual dataset and optimized for long-context financial reports. The model’s performance was enhanced through techniques like Rotary Positional Encoding and Group Query Attention, allowing it to accurately assess qualitative data and identify high-risk firms. By analyzing these sections, the GLM-4 model evaluates the overall risk profile of each company. The findings demonstrate a correlation between companies identified as high-risk by the LLM and those classified as Special Treatment (ST), indicating the model’s potential in providing risk assessment from non-quantitative sentences. © 2024 Copyright held by the owner/author(s).

关键词： Company annual reports

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PL-attack: Projected Light-based Adversarial Attacks against Deep Neural Networks in the Physical World 24

PL-attack: Projected Light-based Adversarial Attacks against...

引用

3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Prior Knowledge Distillation Network for Face Super-Resolution 24

Prior Knowledge Distillation Network for Face Super-Resoluti...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Yang, Qiu Sun, Xiao Li, Xinyu Cui, FengQi Guo, YuTong Hu, ShuangZhen Luo, Ping Li, SiYing School of Computer and Information Hefei University of Technology China Institute of Advanced Technology University of Science and Technology of China China

ISBN: (纸本)9798400718120

The purpose of face super-resolution (FSR) is to reconstruct high-resolution (HR) face images from low-resolution (LR) inputs. With the continuous advancement of deep learning technologies, contemporary prior-guided FSR methods initially estimate facial priors and then use this information to assist in the super-resolution reconstruction process. However, ensuring the accuracy of prior estimation remains challenging, and straightforward cascading and convolutional operations often fail to fully leverage prior knowledge. Inaccurate or insufficiently utilized prior information inevitably degrades FSR performance. To address this issue, we propose a prior knowledge distillation network (PKDN) for FSR, which involves transferring prior information from the teacher network to the student network. This approach enables the network to learn priors during the training stage while relying solely on low-resolution facial images during the testing stage, thus mitigating the adverse effects of prior estimation inaccuracies. Additionally, we incorporate robust attention mechanisms to design a parsing map fusion block that effectively utilizes prior information. To prevent feature loss, we retain multi-scale features during the feature extraction stage and employ them in the subsequent super-resolution reconstruction process. Experimental results on benchmark datasets demonstrate that our PKDN approach surpasses existing FSR methods in generating high-quality face images. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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M-Patch: Adversarial Patch-based Physical Attack against Multi-modal Fusion Detectors 24

M-Patch: Adversarial Patch-based Physical Attack against Mul...

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3rd international conference on algorithms, data mining, and information technology, admit 2024

作者： Chen, Yixin Zheng, Haibin Chen, Jinyin Huang, Haojie College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

As reliance on multimodal fusion object detection models grows in critical tasks, ensuring their robustness and security is essential before deployment. However, research on adversarial attacks in multimodal target detection is still in its early stages, making it crucial for evaluating and improving system resilience. To address these challenges, we propose M-Patch, an adversarial patch-based attack method specifically designed for multimodal fusion object detection models. M-Patch can execute attacks in both digital and physical environments and is applicable to various multimodal detection models. In digital tests, M-Patch achieved success rates exceeding 60%, with a notable 97.6% success rate against the MFEIF+YOLOv3 multimodal detection model. Physical experiments further validated the patch’s effectiveness, demonstrating strong attack results. More importantly, M-Patch reveals significant vulnerabilities in multimodal systems, offering a practical and efficient approach to enhancing their security. © 2024 Copyright held by the owner/author(s).

关键词： adversarial attack

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